Sunday, May 22, 2011

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The infrastructure-less and the dynamic nature of mobile ad hoc networks (MANETs) demands new set of networking strategies to be implemented in order to provide efficient end-to-end communication. MANETs employ the traditional TCP/IP structure to provide end-to-end communication between nodes. However, due to their mobility and the limited resource in wireless networks, each layer in the TCP/IP model requires redefinition or modifications to work efficiently in MANETs.

Routing is a challenging task in MANETs due to the adaptive and dynamic nature of these networks and the Swarm Intelligence approach is considered a successful design paradigm to solve the routing problem. Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviours of insects and of other animals.

Swarm Intelligence is a property of natural and artificial systems involving multiple individuals interacting with each other and the environment to solve complex problems exhibiting a collective intelligent behaviour. Examples of systems studied by swarm intelligence are colonies of ants and termites, schools of fish, flocks of birds, herds of land animals.

Swarm intelligence has a multidisciplinary character. It is usual to divide swarm intelligence research into two areas according to the nature of the systems under analysis: in natural swarm intelligence research biological systems are studied while in artificial swarm intelligence human artifacts are studied. A different classification of swarm intelligence research can be given based on the goals that are pursued: it is possible to identify a scientific and an engineering stream. The goal of the scientific stream is to model swarm intelligence systems in order to understand the mechanisms allowing a system to behave in a coordinated way as a result of local individual-individual and individual-environment interactions. On the other hand, the goal of the engineering stream is to employ the biological behaviours in order to design systems able to solve problems of practical relevance.The typical swarm intelligence system has the following properties:

it is composed of many individuals;

the individuals are either all identical or belong to a few typologies;

the interactions among the individuals are based on simple behavioural rules that make use of local information exchanged directly or via the environment;

the overall behaviour of the system results from the interactions of individuals with each other and with their environment.

The characterizing property of a swarm intelligence system is its capability to act in a coordinated way without the presence of a coordinator. In nature there are many examples of swarms performing some collective behaviour without any individual controlling the group. Wasps build nests with a highly complex internal structure that is well beyond the cognitive capabilities of a single wasp. Termites build nests whose dimensions can reach many meters of diameter and height. When compared to a single termite, which can measure as little as a few millimetres, these nests are huge. Schools of fish and flocks of birds are other examples of highly coordinated groups. Scientists have shown that these elegant behaviours can be understood as the result of a self-organized process where there is no leader and each individual bases its movement decisions solely on locally available information: the distance, the perceived speed, and the direction of movement of neighbours. The most interesting swarm-level behaviours belongs to ants. What is fascinating is that ants are able to discover the shortest path to a food source and to share that information with another ants through stigmergy. Stigmergy is a form of indirect communication used by ants in nature to coordinate their problem-solving activities. Ants realize stigmergetic communication by depositing on the ground a chemical substance called pheromone that induces changes in the environment which can be sensed by other ants. From the observation of real ant colonies, ant algorithms were inspired and applied to many different optimization problems. The main advantages of the swarm intelligence approach compared with a classical approach are the following:

flexibility: the group can quickly adapt to a changing environment;

robustness: even when one ore more individuals fails, the group can still perform its tasks;

self organisation: the group needs relatively little supervision or top down control.

These properties make swarm intelligence a successful design paradigm. Below are illustration to help visualize this power design paradigm.